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Protein Function Analysis through Machine Learning
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein functio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496392/ https://www.ncbi.nlm.nih.gov/pubmed/36139085 http://dx.doi.org/10.3390/biom12091246 |
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author | Avery, Chris Patterson, John Grear, Tyler Frater, Theodore Jacobs, Donald J. |
author_facet | Avery, Chris Patterson, John Grear, Tyler Frater, Theodore Jacobs, Donald J. |
author_sort | Avery, Chris |
collection | PubMed |
description | Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics. |
format | Online Article Text |
id | pubmed-9496392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94963922022-09-23 Protein Function Analysis through Machine Learning Avery, Chris Patterson, John Grear, Tyler Frater, Theodore Jacobs, Donald J. Biomolecules Review Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics. MDPI 2022-09-06 /pmc/articles/PMC9496392/ /pubmed/36139085 http://dx.doi.org/10.3390/biom12091246 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Avery, Chris Patterson, John Grear, Tyler Frater, Theodore Jacobs, Donald J. Protein Function Analysis through Machine Learning |
title | Protein Function Analysis through Machine Learning |
title_full | Protein Function Analysis through Machine Learning |
title_fullStr | Protein Function Analysis through Machine Learning |
title_full_unstemmed | Protein Function Analysis through Machine Learning |
title_short | Protein Function Analysis through Machine Learning |
title_sort | protein function analysis through machine learning |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496392/ https://www.ncbi.nlm.nih.gov/pubmed/36139085 http://dx.doi.org/10.3390/biom12091246 |
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